Incentivizing sharing in social networks

ABSTRACT

An online incentive network is optimized. Online incentive network data is accessed including data regarding sharing between users of the network from at least one database. Inputted parameters and network data are applied to one or more models for incentivized deal sharing to determine profitability of an incentivized sharing deal to offer to plurality of users via an online network for one or more products or services provided by a merchant. The incentivized sharing deal comprises a deal in which a user purchases a particular deal, and then when the user shares the particular deal with a threshold number of other users who also purchase the deal, the user receives an incentive. The parameters include a price of the deal; a proportion of the deal kept by the merchant or the online incentive network, and the number of incentivized users. An interface offers the incentivized sharing deal to users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application No. 61/682,602, titled “Incentivized Sharing inSocial Networks” filed on Aug. 13, 2012 in the U.S. Patent and TrademarkOffice and U.S. Provisional Application No. 61/780,006, titled“Incentivized Sharing in Social Networks” filed on Mar. 13, 2013 in theU.S. Patent and Trademark Office, each of which are herein incorporatedby reference in their entirety for all purposes.

TECHNICAL FIELD

The disclosed subject matter relates to the optimization of sharing inonline social networks.

BACKGROUND

As a myriad of websites centered around social media and social networkshave appeared online over the last few years, a change in the way peopleshare information with one another has occurred. Companies, such asFacebook, Google, Amazon, and LivingSocial, have created websites wheremillions of users can communicate and share content with one another.This communication includes writing emails and reviews, maintainingfriendships, and sharing photos, music, news, products and services.

In recent years, companies have started to leverage online social mediain order to increase awareness and adoption of their products andservices. Many companies encourage sharing behavior between individuals.In addition to offering functionality for users to share content,certain companies offer monetary incentives to their customers to invitefriends to join their websites. By doing this they utilize users' socialnetworks to rapidly increase the volume of site users. Indeed, viralsharing of content can increase traffic to their websites, potentiallyresulting in increased revenue through ads or product sales in a processknown as viral marketing. To further exploit the benefits of viralmarketing, companies can incentivize users by giving them monetaryrewards for adopting a new product or service. The majority of viralmarketing strategies approach incentivization by concentrating onidentifying a relatively small set of individuals with high networkvalues and giving them a discounted or free product, in the hope thatthey would influence many others to also adopt this product.

For example, an online movie rental company may give $20 to a user whosefriend signs up for their service. In addition, the friend receives thefirst one-month subscription for free. Similarly, another onlinemarketplace for designer items may provide tiered incentives forinviting friends to join and purchase from their company, by giving a$30 credit to a user when 10 friends have joined, $30 more in creditwhen 25 friends have joined, free shipping when 50 friends have joined,and $25 to the user when a friend makes his or her first $25 purchase.

In contrast to viral marketing strategies, which rely on identifyinginfluential members, companies have recently started to offer allcustomers monetary incentives to convince their friends to join anonline service or adopt a product, but withhold the reward until theincentive goal (e.g., a friend adopting) is completed. By doing this,they utilize users' social networks to rapidly increase the volume ofsite users.

Besides priming users with incentives for attracting new users, severalsites tie monetary incentives into sharing (or recommending) productswith new and existing customers and rewarding the recommenders when thereferred customers purchase products from the site. For example, userswho share a deal with their friends get credit equal to the price ofthat deal towards future purchases if at least two of their friends buythe recommended deal. A more conservative, but immediately applicable,post-purchase incentive called Me+3 provides that when a user persuadesthree (or more) friends to purchase a particular deal, the user isrefunded their own purchase of the deal.

Such incentives can have measurable impacts on the behavior of users andcan be of benefit to both users and companies. For instance, using theabove example, a user with only one recommendation recipient in mind maychoose to expand the number of recommendations or shares, with theintent of convincing two additional people to purchase the deal andreceiving the deal for free. On the other hand, the company can getadditional purchasers through these recommendations, and recoup the costof the free deal, as well as increase profits and grow their potentialpool of buyers for future deals.

While such incentives become increasingly more popular among onlineretailers, if a deal is not properly structured, the company can standto lose money, which is undesired. In addition, it is often difficult todetermine which incentivization works without actually giving theincentivization to users and monitoring the results. However, this againexposes the provider to less the optimal profits or even a loss.Conventional viral marketing strategies approach this problem byconcentrating on identifying a relatively small set of individuals withhigh network values and incentivizing them to adopt a product, in thehope that they would influence many others to also adopt this product.However, this method doesn't guarantee the highest possible or maximumprofits or even success.

SUMMARY

In some general aspects, a computer-implemented method of generating anincentivized sharing deal includes: receiving, at a server, a request togenerate an incentivized sharing deal for one or more products orservices provided by a merchant, wherein the incentivized sharing dealcomprises a deal in which a user purchases a particular deal, and thenwhen the user shares the particular deal with a threshold number ofother users who also purchase the deal, the user receives an incentive;obtaining, at the server, a plurality of parameters for the incentivizedsharing deal, the plurality of parameters comprising a location at whichthe incentivized sharing deal can be used and an open period for theincentivized sharing deal; determining, based on the plurality ofparameters for the incentivized sharing deal, the threshold number ofother users that are optimal for the deal such that one or moreadditional parameters for the incentivized sharing deal are optimized,wherein the one or more additional parameters comprises one or more ofrevenue from the incentivized sharing deal; profit or profit margin fromthe incentivized sharing deal, and volume of sales for the incentivizedsharing deal; and providing the incentivized sharing deal to a pluralityof users via an online network.

The plurality of parameters for the incentivized sharing deal mayinclude one or more of a price for the incentivized sharing deal and avalue of the incentivized sharing deal.

The method may further include determining, based on the plurality ofparameters for the incentivized sharing deal, a price for theincentivized sharing deal such that the one or more additionalparameters for the incentivized sharing deal are optimized.

In another general aspect, a service provider system includes: one ormore storage devices configured to store network data including dataabout user interaction with the service provider system in one or moredatabases and to store one or more applications configured to implementan incentivized sharing deal; and one or more processing devicesconfigured to access the one or more storage devices including thenetwork data and the one or more applications, and configured to executethe one or more applications causing the processing devices to: receivea request to generate an incentivized sharing deal for one or moreproducts or services provided by a merchant, wherein the incentivizedsharing deal comprises a deal in which a user purchases a particulardeal, and then when the user shares the particular deal with a thresholdnumber of other users who also purchase the deal, the user receives anincentive; obtain a plurality of parameters for the incentivized sharingdeal, the plurality of parameters comprising a location at which theincentivized sharing deal can be used and an open period for theincentivized sharing deal; determine, based on the plurality ofparameters for the incentivized sharing deal, the threshold number ofother users that are optimal for the deal such that one or moreadditional parameters for the incentivized sharing deal are optimized,wherein the one or more additional parameters comprises one or more ofrevenue from the incentivized sharing deal; profit or profit margin fromthe incentivized sharing deal, and volume of sales for the incentivizedsharing deal; and provide the incentivized sharing deal to a pluralityof users via an online network.

The plurality of parameters for the incentivized sharing deal mayinclude one or more of a price for the incentivized sharing deal and avalue of the incentivized sharing deal.

Based on the plurality of parameters for the incentivized sharing deal,a price for the incentivized sharing deal is determined such that theone or more additional parameters for the incentivized sharing deal areoptimized.

In yet another general aspect, a computer implemented method ofoptimizing an online incentive network includes: accessing onlineincentive network data including data regarding sharing between users ofthe network from at least one database; inputting parameters and thenetwork data to one or more processing devices; applying, by one or moreof the processing devices, the parameters and the network data to one ormore models for incentivized deal sharing to determine profitability ofan incentivized sharing deal to offer to plurality of users via anonline network for one or more products or services provided by amerchant, wherein the incentivized sharing deal comprises a deal inwhich a user purchases a particular deal, and then when the user sharesthe particular deal with a threshold number of other users who alsopurchase the deal, the user receives an incentive; and providing anonline or mobile interface configured to offer the incentivized sharingdeal to a plurality of users.

The parameters may include a price of the deal; a proportion of the dealkept by the merchant or the online incentive network, and the number ofincentivized users.

The model may include an arrival function configured to determine thenumber of users entering the network. The model may include an awakenfunction configured to determine how many existing users purchase adeal. The model may include an incentive function configured todetermine how many users are incentivized. The model may include asharing function configured to determine how many users the incentivizedusers share the deal with. The model may include a recipient functionconfigured to determine how many recipients of the shared deal purchasethe shared deal. The model may include a cost function configured todetermine how many users who shared the deal receive the incentive.

In yet another general aspect, an online service provider systemconfigured to optimize an online incentive network includes: one or morestorage devices configured to store online incentive network dataincluding data regarding sharing between users of the network; one ormore storage devices including one or more applications configured tooptimize incentivized deal sharing for one or more products or servicesprovided by a merchant, wherein the incentivized sharing deal comprisesa deal in which a user purchases a particular deal, and then when theuser shares the particular deal with a threshold number of other userswho also purchase the deal, the user receives an incentive; and one ormore processing devices configured to: receive one or more inputparameters and the network data; apply the parameters and the networkdata to one or more models for incentivized deal sharing to determineprofitability of an incentivized sharing deal to offer to plurality ofusers via an online network; and provide an online or mobile interfaceconfigured to offer the incentivized sharing deal to a plurality ofusers.

The parameters may include: a price of the deal; a proportion of thedeal kept by the merchant or the online incentive network; and thenumber of incentivized users.

The model may include an arrival function configured to determine thenumber of user entering the network. The model may include an awakenfunction configured to determine how many existing users purchase adeal.

The model may include: an incentive function configured to determine howmany users are incentivized; a sharing function configured to determinehow many users the incentivized users share the deal with; a recipientfunction configured to determine how many recipients of the shared dealpurchase the shared deal; and a cost function configured to determinehow many users who shared the deal receive the incentive.

The details of various embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the following description, the drawings, and theclaims.

DRAWING DESCRIPTION

FIG. 1 is a block diagram of an example of an online social networksystem;

FIG. 2 is a block diagram of hardware components of an example of onlinesocial network offer deals and optimization of incentivization for thesystem of FIG. 1;

FIG. 3 is a flow chart of a procedure performed by the online socialnetwork system of FIGS. 1 and 2;

FIG. 4 is a screen shot of deal voucher presented to a buyer in theonline social network system of FIGS. 1 and 2;

FIG. 5 is a flow chart of a procedure performed by the deal machine ofthe online social network system of FIGS. 1 and 2;

FIGS. 6-10 are screen shots of a deal website created and managed by thedeal machine of the online social network system of FIGS. 1 and 2;

FIG. 11 is a screen shot of an electronic mail sent to a friend of abuyer in the online social network system of FIGS. 1 and 2;

FIGS. 12a-12g show examples of distribution graphs used by the modelingand optimization processes;

FIG. 13 is a flow chart of an example of an optimization process for thesystems offering incentivized deals for a social network of the systemof FIGS. 1 and 2;

FIG. 14 is a flow chart of an example of a modeling process for theoptimization process of FIG. 13;

FIGS. 15a, 15b, and 15c illustrate the results when the percentage ofthe profits the broker receives is varied;

FIG. 15d shows the performance when the reward given back to theindividual is limited to 20% of the deal price;

FIG. 16a illustrates the effects when increasing the percentage ofincentivized users for Me+2 and Me+3;

FIG. 16b illustrates the effect on the number of shares when increasingthe volume for Me+2 and Me+3; and

FIGS. 16c and 16d illustrate linearly increasing the reward andpercentage of incentivized users to show the change in profits andincrease in shares, respectively.

DESCRIPTION

Overview

The following description provides techniques, models, methods, andsystems for incentivization modeling of sharing in networks. Themodeling provides simulation and refinement of incentivized deals topromote sharing between users without the need for expensive large-scalesocial experiments that would otherwise be required to determine theeffect of any incentivization scenario for any particular network.

In particular, simulation and optimization or improvements forprofitability is provided for the sharing model for Me+N. Me+N is anincentivized sharing model that provides a user, after purchasing aproduct, a reward if the user convinces N or more others to purchase thesame product or service. The optimization is able to account for anumber of factors that may change the overall profitability of thesharing model in any particular social network. Examples of three suchfactors include 1) the reward amount, 2) product/service profit margin,and 3) the threshold N for the number of adopting friends that resultsin receiving the reward. However, one will appreciate that the methodsand systems described herein can incorporate other factors, as discussedin further detail below.

In contrast to viral marketing strategies, which rely on identifyinginfluential members, companies have recently started to offer allcustomers monetary incentives to convince their friends to join anonline service or adopt a product, but withhold the reward until theincentive goal (e.g., a friend adopting) is completed. By doing this,they utilize users' social networks to rapidly increase the volume ofsite users.

Besides priming users with incentives for attracting new users, severalsites tie monetary incentives into sharing (or recommending) productswith new and existing customers and rewarding the recommenders when thereferred customers purchase products from the site. For example, userswho share a deal with their friends get credit equal to the price ofthat deal towards future purchases if at least two of their friends buythe recommended deal. A more conservative, but immediately applicable,post-purchase incentive called Me+3 provides that when a user persuadesthree (or more) friends to purchase a particular deal, the user isrefunded their own purchase of the deal.

Such incentives can have measurable impacts on the behavior of users andcan be of benefit to both users and companies. For instance, using theabove example, a user with only one recommendation recipient in mind maychoose to expand the number of recommendations or shares, with theintent of convincing two additional people to purchase the deal andreceiving the deal for free. On the other hand, the company can getadditional purchasers through these recommendations, and recoup the costof the free deal, as well as increase profits and grow their potentialpool of buyers for future deals.

While such incentives become increasingly more popular among onlineretailers, if a deal is not properly structured, the company can standto lose money, which is undesired. In addition, it is often difficult todetermine which incentivization works without actually giving theincentivization to users and monitoring the results. However, this againexposes the provider to less the optimal profits or even a loss.Conventional viral marketing strategies approach this problem byconcentrating on identifying a relatively small set of individuals withhigh network values and incentivizing them to adopt a product, in thehope that they would influence many others to also adopt this product.

As pointed out above, sharing scenarios to maximize, optimize, orimprove a network's profits are often hard to predict. As a result,significant investments in expensive large-scale social experimentswould be required to determine results for any particular combinationsof products and rewards given any number of different factors includingthose listed above. However, the techniques, models, methods, andsystems provided herein allow determinations to optimize deals forproducts on various networks through simulations that may be quickly runand determined based on a network's own data. In addition, parametersmay be easily changed to determine an optimized scenario for anyparticular network, product, or deal.

Incentivized Deals

FIG. 1 shows one example of an online commerce system 100 that isdesigned to promote goods and services. The online commerce system 100includes one or more user devices 101, one or more service providersystems 110, and various communication paths 115 that provide contentand data between users and the service provider system 110 and otherusers, for example. In particular, the service provider system 110provides online deals through an interface provided by the user device101, such as a browser or mobile application interface. The deal isprovided to a buyer 125 to incentivize the buyer 125 to share the dealwith or to refer the deal to other people (e.g., friends, family,acquaintances, colleagues, and others) via some form of electroniccommunications (e.g., e-mail, text messaging, or other online socialmedia, such as Twitter, Facebook, Skype, Blogs, etc). The deal is anincentive (e.g., a reward or a monetary discount) given for one or moreitems or services for sale by a merchant 130 at a store 135. The dealmay be offered for a set period of time (e.g., 24 hours). In the exampleshown in FIG. 1, the merchant 130 enters into a relationship 140 with anonline service provider, for example, a deal broker 145 of a dealbrokerage 147. In this example, the deal is offered by the dealbrokerage 147 via the service provider system 110. For example, awebsite hosted by the service provider system 110 is created and managedby the service provider system 110. The website is controlled by oraccessible to one or more deal brokers 145. The buyer 125 communicateswith the service provider system 110 through a communication path 115.In particular, each buyer 125 accesses the deal website hosted by theservice provider system 110 via a communications network provided by thecommunication path using a browser or a mobile application running on auser device 101.

As discussed in greater detail below, the deal website provides aninterface through which the buyer 125 can obtain information about thedeal, for example, the location at which the deal can be used, the priceof deal, a description of the item or items for sale in the deal, or thetime during which deal is offered.

The item or items for sale associated with a deal can be a product(e.g., food, article of manufacture) or a service (e.g., cleaningservices or transportation services) or groups of products or groups ofservices. The deal website enables the buyer 125 to purchase the dealand then share the deal with others.

The user device 101 may be any type of electronic device that presents,plays, or renders content, such as a web page accessed from the Internetor World Wide Web for a user. Typically, a user device is a computerdevice includes one or more processing devices as described below. Forexample, the user device may be a consumer electronics device, a mobilephone, a smart phone, a personal data assistant, a digital tablet/padcomputer, a hand held/mobile computer, a personal computer, a notebookcomputer, a work station, a vehicle computer, a game system, aset-top-box, or any other device that can implement a user interfaceand/or browser to communicate with, access, and present content from theInternet or World Wide Web to a user and/or communicate over a thecommunication path 115 to access the service provider system 110.

The service provider system 110 includes one or more programmableprocessing devices that are able to respond to instructions in awell-defined manner and can execute instructions (such as found in aprogram or engine). The service provider system 110 includes electronicand digital components, including hardware needed to implement theinstructions and read/access data. The service provider system 110includes a communication mechanism that is able to communicate with theuser devices 101 through a communication path 115. Additionally, theservice provider system 110 includes one or more components, such as aweb server that hosts and manages the deal website. The deal website canbe public (viewable by anyone on the Internet) or private (viewable onlyby persons who have provided identifiable information and have receiveda unique identification number). Details of the service provider system110 are shown in FIG. 2 and are discussed below with reference to FIG.2.

The deal brokerage 147 is a party that offers the deal in a deal websiteprovided by the service provider system 110. In one example, the dealbrokerage 147 does not maintain an inventory of goods or services.Rather, it is the merchant 130 who maintains the inventory, for example,at the merchant store 135 or at a location such as a warehouse that maybe distinct from the merchant store 135. The merchant store 135 can be abrick and mortar store physically located near to the buyer 125 or tosomeone else (such as a recipient 165 of the deal) who will be receivingthe deal obtained by the buyer 125. The store 135 can alternatively oradditionally include a virtual (that is, online) store accessiblethrough the communication paths 115.

Referring to FIG. 2, one example of the hardware components of theonline commerce system 100 are shown along with their relationships toeach other. In particular, each user device 101 includes a networkbrowser 200 or mobile application 201 that provides a user interface onthe user device 101 to permit access to and interaction with the dealwebsite. The deal website is displayed or accessible by the networkbrowser 200 or mobile application 201.

In one example, the service provider system 110 includes one or moreprocessing devices with supporting memory and communicationsinfrastructure/interfaces that are able to communicate with the userdevices 101 via the communication paths 115. In one example, theprocessing devices may be one or more application server(s) 205. The oneor more application servers may be protected by one or more securitydevices, such as a firewall 210. The application server 205 includes acombination of hardware and software to provide and manage the dealoffers to the interface of the user devices 101 via the communicationpaths 115. The application server 205 can include a combination ofmemory and input/output devices. The application server 205 is connectedto one or more applications or software engines, such as an electronicmail (email) engine 215, a referral engine 220, a social engine 225, anecommerce engine 230, and an optimization engine 235. In one example,these engines may be implemented by one or more additional servers.Additionally, the application server 205 can be connected to a database236. Additionally, each engine 215, 220, 225, 230, 235 can include itsown dedicated database 217, 222, 227, 232, 237 that can be accessible bythe application server 205. The email engine 215, the referral engine220, and the ecommerce engine 230 are connected to a billing engine 240,which can also include a dedicated database 242.

The optimization engine 235 may be connected to all the engines anddatabase in order to access information and data used in optimizationprocessing and modeling. The engine 235 implements an optimizationprocess, such as those described in greater detail below with regard toFIGS. 13 and 14.

It will be appreciated that the examples in FIGS. 1 and 2 are given forillustrative purposes only, and that many different configurations,combinations of devices, and numbers of devices may be provided for anyparticular commerce system 100. For example, although only oneapplication server (e.g., 205) and associate database 236 are shown, itwill be appreciated that in actual implementation other configurationsof servers or combining of servers may be provided or banks of a numberof these machines as necessary may be provided to handle the number ofusers, data, and traffic that a particular implementation of a serviceprovider system 110 handles.

In addition, other types of hardware may be used, such as various typesof computers featuring any of various processors running any of severaloperating systems. In addition, service provider system 110 may beconfigured to provide a location that is centralized and/or remote fromthe perspective of the user. The central location may be one physicallocation, such as a room, building, or campus providing the hardwarecomponents of the system 110. However, in reality the central locationmay be virtual or distributed where services are provided, content areaccessed from, and data is stored over a number of distributed systemsand/or geographic locations. In other words, although the serviceprovider system 110 is shown in FIG. 1 as being a single entity, otherconfigurations wherein the applications, processing, services, content,and data are distributed both in terms of devices and geographicallocation are within the meaning of the service provider system 110.Additionally, the service provider system 110 may use third parties tohost the web page or website and may initiate services as needed ondemand, for example, using cloud computing wherein the locations andstructure providing the services change size, and physical location,distribution, and even equipment dynamically over time.

Referring to FIG. 3, a procedure 300 is performed to provide an onlinebuying experience to the buyers 125. To facilitate understanding of theprocedure 300, reference is made to FIG. 4, which shows a screen shot400 of a deal voucher sent by email (or any other suitable communicationmethod) to a buyer 125 for a deal offered for an item or items fromfictional merchant Yogery Café; FIGS. 6-10, which show screen shots of adeal website 605 hosted by the service provider system 110 to provide adeal on items for the Yogery Café, and to FIG. 11, which shows an email1100 sent by the service provider system 110 to a friend of the buyer125. In the example of FIGS. 4-11, the deal is a monetary discount (forexample, 50%) off a voucher having a certain value for items such asyogurt and toppings sold by Yogery Café. Thus, in this example, a buyer125 who accepts the deal offer purchases a voucher at a price of $10that is good toward the purchase of $20 worth of yogurt and toppings atYogery Café.

Initially in the procedure 300, after the deal broker 145 and themerchant 130 have established the business relationship 160, the dealbroker 145 makes an agreement with the merchant 130 to promote an itemor items for sale on the deal website hosted by the service providersystem 110 for a period of time (305). The agreement specifies terms ofthe offer that the deal brokerage 147 will advertise on the dealwebsite. For example, the deal broker 145 makes an agreement with YogeryCafé to promote the $20 voucher on the deal website for a period of timeof 24 hours.

Next, the service provider system 110 (specifically, application server205 with assistance from one or more of the email engine 215, thereferral engine 220, the social engine 225, and the ecommerce engine230) runs the deal on the item or items for the period of time (310), asdetailed in the procedure 310 of FIG. 5 and as shown in the exemplaryscreen shots of FIGS. 6-10. For example, the service provider system 110is configured to promote the deal on the deal website, receiveacceptances of the deal from the buyer 125 through the deal website,receive payment by the buyer 125, and confirm the deal acceptancesthrough email or on a separate browser to the buyer 125. Duringprocessing 310, and as discussed in detail with reference to FIG. 5, theservice provider system 110 also notifies each buyer 125 that the buyer125 would obtain a greater monetary discount (for example, 75 or 100%off) on the voucher for the item or items if enough (for example, atleast N) friends of the buyer 125 purchase the same deal after beingprompted by the buyer 125 through a dedicated and identifiable link ormessage. This is described herein as an incentivized deal whichencourages a purchaser of the deal to share the deal with others.

After the online deal is completed (that is, after the expiration ofperiod of time for which the online deal was offered), the serviceprovider system 110, and in particular, the application server 205 withassistance from the billing engine 240, performs a billing procedure 315for determining how much to charge each buyer 125 who accepted the deal.

Initially, the service provider system 110 selects a buyer 125 from theset of buyers who accepted the deal (320). The service provider system110 determines if a referred purchase count for the selected buyer isgreater than a predetermined value, for example, N (325). The referredpurchase count indicates how many other buyers purchased the deal upon aprompt or referral by the selected buyer, as detailed in the descriptionwith reference to FIG. 5. The billing engine 240 accesses informationabout the referred purchase count from the referral engine 220, whichtracks the count in its own database 222 or in the database 235.

If the service provider system 110 determines that the referred purchasecount for the selected buyer is not greater than the predetermined value(325), then the service provider system 110 charges the selected buyerfor the deal at the original monetary discount (335). For example, ifthe original deal is a monetary discount of 50% off a single $20voucher, then the selected buyer would be charged $10. The serviceprovider system 110 can perform the charge transaction using anysuitable ecommerce system, such as, for example, a credit card paymentor a payment intermediary service (such as PayPal). In particular, thebilling engine 240 accesses the ecommerce engine 230, which isresponsible for collecting and processing payment information of thebuyer when the deal is purchased. The billing engine 230 instructs theecommerce engine 230 on which buyer to charge and a quantity and amountto charge to the buyer's account. In some implementations, the ecommerceengine 230 may control another ecommerce system through which the buyer125 has chosen to make payments. The billing engine 240 pulls thebilling information from the ecommerce engine 230 so that the serviceprovider system 110 can effectuate the charge to the buyer's account.

If the service provider system 110 determines that the referred purchasecount for the selected buyer is greater than the predetermined value(325), then the service provider system 110 (through the billing engine240) charges the selected buyer for the deal at a new monetary discountthat is greater than the original monetary discount (330). For example,if the original deal is a monetary discount of 50% off a single $20voucher and the new monetary discount is 100% off a single $20 voucher,then the selected buyer would not be charged. As another example, if theoriginal deal is a monetary discount of 50% off a single $20 voucher andthe new monetary discount is 75% off a single $20 voucher, then theselected buyer would be charged $5.

The new monetary discount can be any discount that is greater than theoriginal monetary discount. The new monetary discount can be dependenton the way in which the buyer shared the deal with the other people whoeventually purchased the deal. For example, a new monetary discount of100% can be given if the buyer shared the deal with people through emailand a new monetary discount of 75% can be given if the buyer shared thedeal with people through a social medium such as Twitter or Facebook.

The service provider system 110 then sends the deal voucher to theselected buyer 125 (340). The deal voucher can be sent by electronicmethods, such as email, messaging, and social media. For example, asshown in FIG. 4, a deal voucher 400 for $20 to spend on frozen yogurtand toppings at Yogery Café(the deal 405) is sent by email to the buyer125 (who, in this example, is named Jane Q. Buyer 410). The deal voucher400 can also include the name 415 of the deal broker 145, informationabout the location 420 of the merchant 115, instructions 425 on how touse the deal voucher 400, and standard terms and conditions 430.

The service provider system 110 determines if all buyers in the set ofbuyers who accept the deal have been selected (345). If the serviceprovider system 110 determines that not all buyers have been selected(345), then the service provider system 110 selects the next buyer fromthe set of buyers who accept the deal (350). If the service providersystem 110 determines that all buyers have been selected (345), then thebilling engine 240 processes an end-of-deal report that is supplied tothe merchant 130 (355), such report can provide information, forexample, about how many buyers received the deal, how many buyersreceived the free deal through referrals, and a check amount for a givenmerchant. In addition, the data from these reports may be used inoptimization modeling to determine the number of others N that the buyer125 needs to share with to obtain the reward, as explained in furtherdetail below. The transfer of the money to the merchant 130 for the dealminus a commission can happen offline. However, it is possible that themoney transfer to the merchant 130 be automatic and performed by thebilling engine 240. For example, if 1000 buyers 125 purchase the dealvoucher at the original monetary discount of 50% off a single $20voucher, 500 buyers 125 purchase the deal voucher at a new monetarydiscount of 100% off a single $20 voucher, and the commission agreed toby the merchant 130 and the deal broker 145 is 30%, then $7000.00 istransferred from the deal brokerage 147 to the merchant 130. Suchtransfer can be performed by the service provider system 110 if doneonline. In this example, the deal broker 145 retains a $3000.00commission.

Referring to FIG. 5, the service provider system 110 performs theprocedure 310 to run the deal on the item or items for the period oftime. Initially, the service provider system 110 through the applicationserver 205, which accesses, for example, the ecommerce engine 225 andthe referral engine 220, promotes the deal for the item or items on thedeal website (500). For example, as shown in the example of FIG. 6, adeal 600 for frozen yogurt and toppings at the merchant Yogery Café ispromoted on a deal website 605. The deal 600 provides a monetarydiscount of 50% off a $20 voucher so that a buyer 125 of the deal wouldonly pay $10 for a $20 voucher than could be used on yogurt and toppingsat the Yogery Café. The deal 600 is displayed in a window 610 that canalso display information about the deal and the merchant, Yogery Café.For example, the window 610 can display an image 615 of the item oritems offered in the deal, an icon 620 displaying the monetary discount,an icon 625 displaying the number of other people who have alreadybought the deal, and an icon 630 displaying the time remaining in theopen period (that is, the time remaining during which the deal can bebought). The window 610 displays a button 635 that enables a personvisiting the deal website 605 to start a transaction to buy the deal600. The window 610 also includes one or more icons 640 that prompt theperson visiting the deal website 605 to share the deal 600 with otherpeople. For example, the icons 640 can display links to social mediasuch as Facebook and Twitter or to email.

The deal website 605 can include other windows, icons, buttons, images,shapes, text, media, video, etc. such as, for example, an access window645. The access window 645 provides a region 650 to enable a person tosign up for an account with the deal broker 145 to get deals inDealville (which is the location at which this deal 600 is being offeredand is the location of the Yogery Café). The access window 645 alsoincludes a link 655 that enables a person who already has an accountwith the deal broker 145 to sign in to his/her account to purchase thisdeal 600 or to login using his/her social networking log-in information.As another example, the deal website 605 can include a location window660 that provides geographic information about the location (in thisexample, Dealville) at which the deal voucher 400 can be used. Thisinformation can include an address, a map, and a link to directions. Thedeal website 605 can include a city window 665 that provides other moregeneral information about the location (in this example, Dealville) atwhich the deal voucher 400 can be used or a user feedback window 670that provides an area in which users can submit comments about the deal600 and/or the location of the deal 600.

The deal website 605 can also include a background image 675 that can beselected to correspond specifically to the location of the deal 600. Forexample, the background image 675 can be a photograph of a part of theDealville skyline.

Next, the service provider system 110 through the application server205, which accesses the ecommerce engine 230, receives an acceptance ofan offer from a buyer 125 (505).

Acceptance of an offer from a buyer 125 (505) can be predicated onrequiring a buyer 125 to log into an account the buyer 125 has set upwith the deal broker 145 through the service provider system 110. Thedeal broker 145 can set up an account with the deal broker 145 at anytime before accepting the offer. The buyer account can include storedinformation about a preferred method of payment used by the buyer 125(for example, a credit card and number of the buyer) in addition to aunique buyer identification code. The buyer account can also specifypreferred cities in which the buyer 125 would be interested in gettinginformation about deals. For example and with reference to FIG. 6, thebuyer 125 can indicate that Dealville is a preferred city and thereforethe buyer 125 would see the Dealville deal whenever the buyer 125accesses the deal website. The buyer 125 can sign in to his/her dealbroker account by, for example, selecting the link 655. The buyer 125can sign in to his/her deal broker account by signing in to a socialmedia account such as Facebook, which can be linked to his deal brokeraccount, by, for example, activating the link 655. If the buyer 125 doesnot have a deal broker account, then the buyer 125 can be prompted tocreate an account through the window 650.

To accept the offer, the buyer 125 selects the “buy now” button 635, atwhich time the service provider system 110 alters the deal website 605to that shown in FIG. 7. The buyer 125 can review the deal 600 in a newdeal window 710 that displays particulars about the deal 600, includingthe price and the quantity 715. Additionally, if at this point, thebuyer 125 has not yet signed in to his/her account, the buyer 125 isgiven another chance to do so at this time through selection of a link755 (which enables direct sign in to the deal broker account), asub-window 700 (which enables access through the social media account),or a sub-window 705 (which enables creation of a new deal brokeraccount).

Referring to FIG. 8, if the buyer 125 has already signed in to his/heraccount in a previous step, then the deal website 605 displays a button800 that enables the buyer 125 to purchase the deal. Additionally, thedeal website 605 displays an account window 805 that providesinformation about the buyer 125 and his/her account. The buyer 125 isalso given the option in a gift sub-window 810 to give the deal as agift to someone else (for example, to the recipient 165).

Referring again to FIG. 5, after the buyer 125 accepts the deal offer(505), the service provider system 110 determines whether the buyerpurchase at 505 is associated with a compound reference code (510). Thecompound reference code (CRC) would be assigned to the buyer 125 if thebuyer 125 accessed this particular deal by selecting a referral fromanother buyer of the deal (this buyer can be referred to as adeal-sharing buyer). The CRC is used to track the referrals of thedeal-sharing buyer to determine whether the deal-sharing buyer'sreferrals result in a predetermined number of deal purchases by thebuyer's friends. The CRC is a set of values that includes the buyeridentification code that is related to the deal-sharing buyer (BIC), anassigned purchase reference code (PRC) that is related to thedeal-sharing buyer, and a destination identifier (DI) that is related tothe manner in which the deal-sharing buyer referred the deal to thisbuyer. Thus, the CRC=[BIC, PRC, DI]. As discussed above, the BIC isassigned to a buyer when the buyer sets up the deal broker account. ThePRC is a code that is assigned the deal-sharing buyer when thedeal-sharing buyer purchases the deal. A PRC will also be assigned tothis buyer later in the procedure 310 as will a CRC if this buyer refersthis deal. The DI is a code associated with how the deal-sharing buyerreferred the deal to this buyer. For example, the DI can be 1 for areferral sent through email, 2 for a referral sent through Facebook, 3for a referral sent through Twitter, 4 for a referral sent through anSMS or MMS text, 5 for a referral sent through an Instant Messagingservice, etc. In this way, the CRC can be used to keep track of thedeals referred by the deal-sharing buyer.

For example, if the buyer 125 is Jane Q. Buyer and she has a BIC of3241, the deal for the Yogery Café that Jane Q. Buyer purchases has aPRC of 41-555, and the DI for the email referral is 1, then the CRCwould be [3241, 41-555, 1].

If the service provider system 110 determines that the buyer purchase at510 is associated with a compound reference code, then the serviceprovider system 110 stores the compound reference code (515) anddetermines if a deal-sharing buyer's purchase can be discerned from thestored CRC (520). The service provider system 110 can make thisdetermination by inspecting each component (that is, the BIC, PRC, DI)of the CRC to determine if each component is a valid component.Presumably if the deal-sharing buyer's purchase cannot be discerned fromthe stored CRC, then it is possible the CRC was fraudulent and in thiscase, the deal-sharing buyer would not be rewarded. If the serviceprovider system 110 determines that the deal-sharing buyer's purchasecan be discerned from the stored CRC (520), then the service providersystem 110 updates the referred purchase count of the deal-sharing buyerby, for example, incrementing the referred purchase count by 1 (525).

Next, the service provider system 110 assigns a unique PRC to this buyerand stores that PRC with the purchase transaction in one of thedatabases accessible to the application server 205 (530). Thus, the PRCis a code from which can be discerned an identification of this buyerand of the item purchased in this transaction.

The service provider system 110 prompts the buyer 125 to share thisparticular deal offer with others using any suitable transmission (suchas email) (535). If the buyer 125 decides to share this deal offer withothers based on the specific prompt then that referral would haveassigned to it the information about this buyer and the item in the dealthat was purchased because the referral would have an assigned PRC inaddition to other information as discussed below. The service providersystem 110 can facilitate the referral (and therefore referralpurchases) by accessing the social engine 225 to transmit messages toother users' social network(s). Next, for each destination through whichthis buyer 125 refers the deal offer, a CRC is created and assigned tothe referral (540). The created CRC includes the BIC (the buyeridentification code), the PRC (the information about this buyer and thedeal), and the DI, the destination identifier.

Specific examples of how the service provider system 110 prompts thebuyer 125 to share this deal are shown in FIGS. 9 and 10. In FIG. 9, thebuyer 125 has just purchased the deal, and the deal website 605 isupdated to include a share window 900 that displays information abouthow this buyer 125 can get this deal at an even greater discount (inthis example, the greater discount is 100% off). The share window 900includes a link 905 that the buyer 125 can share with friends, the link905 being associated with the assigned PRC and also associated with anassigned CRC for this buyer. The share window 900 can also include anemail interface 910 that enables the buyer 125 to send an email tofriends and include a message directly from the share window 900, suchemail would be associated with the assigned PRC and a CRC that isspecific to the email. The share window 900 can include a click-to-shareregion 915 that opens another application (such as social mediaapplications Facebook or Twitter or an email application such asOutlook) that would assign any message sent through these means with aunique PRC and a unique CRC depending on the method used to share thedeal. And, the service provider system 110 can also include a declinebutton 920 that enables the buyer 125 to decline the sharing the deal atthis time.

Referring to FIG. 11, if the buyer 125 shares the deal offer with afriend through email, then the friend receives an email 1100 including alink 1105 that would have a unique CRC including the PRC for the dealpurchased by the buyer 125, the DI for a referral sent through email,and the BIC for the buyer 125 who referred the deal. In the exampleprovided above, the link 1105 for Jane Q. Buyer buying the Yogery Cafédeal 600 and referring it through email would have a CRC of [3241,41-555, 1].

In FIG. 10, if the buyer 125 declines to share by clicking the declinebutton 920, then the service provider system 110 displays a confirmationwindow 1000 in the deal website 605. Among other things, theconfirmation window 1000 includes a purchase confirmation 1005 andanother share window 1010 that displays information about how the buyer125 can get this deal at the greater discount. The confirmation window1000 can also include instructions 1015 to the buyer 125 on how and whento use the deal voucher.

Next in the procedure 310, the service provider system 110 determines ifthe deal time period has expired (545). If the deal time period has notexpired, then the service provider system 110 continues to processpurchases at 505. If the deal time period has expired, then the serviceprovider system 110 performs the billing procedure 315 (FIG. 3).

Referring again to FIG. 1, once the deal is purchased, the buyer 125 canretain the deal for his/herself or the buyer 125 can give the deal tothe recipient 165 (for example, by gifting the deal). The buyer 125 (orthe recipient 165 of the deal if the recipient 165 is not the buyer 125)can use the deal (represented by the arrow 170) by accessing themerchant store 155 either in person (if the store 155 is a brick andmortar store) or virtually (for example, if the store 155 includes anonline presence) before the expiration of the deal voucher. To use thedeal, the buyer 125 presents the deal voucher to the merchant 130 andenables the merchant 130 to scan or read the deal voucher to check thatit is valid (that is, not expired and not fraudulent). For example, thebuyer 125 can print the deal voucher 400 and take it to the Yogery Café(which is the merchant store 155). As another example, the buyer 125 canlet the merchant 130 electronically scan a code that is unique to thedeal voucher and is displayed on a portable electronic device (such as asmartphone) of the buyer 125. The buyer 125 (or recipient 165) can waitto use the deal voucher as long as the buyer 125 (or recipient 165) usesthe deal voucher before the expiration of the deal voucher.

The online commerce system 100 incentivizes each buyer to share the dealwith other people. Moreover, the system 100 is designed so that eachbuyer will have the same incentive throughout the open time period thatthe deal is offered. Thus, the first buyer to purchase the deal has thesame incentive to share as the 1000^(th) buyer to purchase. Thus, the1000^(th) buyer to purchase can still obtain the same greater discountas the first buyer to purchase as long as the 100^(th) buyer shares thedeal with enough other people. Thus, the online commerce system 100maintains the incentive to share throughout the open time period thatthe deal is offered. The only possible diminishment to incentive toshare is the nearing expiration of the open time period, but that wouldoccur only if the open time period is made known to those who visit thedeal website. That is, a buyer who buys the deal with one minuteremaining in the open time period may not have any incentive to sharesince it is unlikely that sharing would result in a greater discount forsuch a late-buying buyer if the deal website provides the open timeperiod. In some implementations, the deal website displays or providesthe open time period. In other implementations, the deal website doesnot display or provide the open time period. Thus, the online commercesystem 100 provides a persistent incentive to share the deal, and theincentive could only decline if the buyer is aware of the open timeperiod.

The deal broker 145 need not notify the merchant 130 of the incentiveand the greater discount offered to buyers who share enough deals thatare subsequently purchased can be paid by the deal broker 145, not themerchant 130. The deal broker 145 gets the benefit of more people buyinginto the deal even though the deal broker 145 absorbs the greaterdiscount provided to deal-sharing buyers.

Optimization

The deal recommendations between users described above reveal a socialnetwork structure that exhibits communication patterns between users.One important structural property of social networks is the degree of auser, which may be defined as the number of users someone is connectedto. Numerous studies show that degree distributions often follow a powerlaw distribution. However, monetary incentives modify this distribution.A temporal graph evolution model that mimics observed sharing patternsand allows generation of new data for the simulation of a wide range ofincentive scenarios is provided by the service provider system 110through one or more optimization processes.

The optimization processes are implemented using an optimization engine235 of the service provider system 110. The optimization engine 235accesses network data for a particular network (e.g., data in thedatabases 217, 222, 227, 232, 236, 237, and/or 242, regarding the usersof the service provider system 110) and applies various graph modelsand/or distributions to the dataset to accurately determine likelyoutcomes of various incentive scenarios. Profitability associated withthese scenarios may be used to generate a decision matrix. The decisionmatrix can be used to compare various scenarios and parameters, and toselect a scenario for implementation as a deal provided by the serviceprovider system. In addition, feedback through additional accumulateddata may be used to modify the scenario based on actual performanceassociated with the deal. As a result, ongoing feedback may beincorporated to further refine or select a model or scenario thatoptimizes the broker's profits or increases the broker's profits to a anacceptable threshold level.

According to the methods and systems provided herein, sharing incentivesare offered to all users of the service provider system. Although thesystem offers the incentives to all users, generally it is the earlyadopters who recommend well that are able to take advantage of theincentive. As a result, the service provider system implements a massviral marketing system (versus a directed one). In contrast, the goal ofmany direct viral marketing campaigns is to incentivize a subset ofindividuals to adopt a product, with the hope that the few influenceothers to do the same.

In following examples, let G−

U, D, P, S

refer to a graph, where u∈U refers to the set of users, d∈D is the setof items or deals that can be purchased, P=U×D are the purchases of adeal by a user and S=

U×U×D

is the deal shares (or recommendations) between users. Each user can bea sender or a recipient of a shared recommendation, and the three-tuples is ordered in terms of the 1) the share sender or origin of the shareo, 2) the recipient r of a deal d, and the deal d that is shared. Asusers send deals to other network users and outside users, a directedsocial network is formed. The set of recipients of a share is defined asR(o, d)={r:

o, r, d

∈S}. In addition, the share outdegree (the number of recipients withwhom sender o shared deal d), is defined as outdegree(o, d)=|R (o, d)|.

In addition, every user has a set of properties, such as their arrivaltime into the network or purchasing preferences, as discussed in moredetail below.

Lastly, every incentive i may be defined by at least two properties,such as, for example: a goal (i.e., an objective that a sharer has toreach) and a reward (i.e., the benefit to the sharer if he reaches thegoal). The cost_(u,d)(i) is the incentive cost to the broker when asharer u who bought deal d achieves the incentive goal and must be giventhe reward.

There are two other types of entities besides users: the deal broker c,which sells the deals through the website of the service providersystem, and the merchant m, which provides the services or products thatare featured in the deal. Each deal has an associated price, and thedeal broker and merchant agree on the deal profit margin (i.e., aproportion of the deal price) that the broker keeps, which is therevenue_d for the broker of the deal d. Therefore, a set of propertiesassociated with every deal d includes properties such as: price,price_(d), and a profit margin, margin_(d) (i.e., a percentage of thedeal price that the broker keeps).

One goal of the optimization provided by the optimization engine is toattempt to maximize the profit for any particular deal scenario.Therefore, in one example, given a set of incentives I, an incentive i*maximizes profits if and only if:

$i^{*} = {\begin{matrix}\max \\i\end{matrix}{\sum\limits_{d \Subset D}{\sum\limits_{u \in {u{({d,i})}}}\left\lbrack {{profit}_{u,d}(i)} \right\rbrack}}}$where D is the set of deals, u(d, i) is the set of users who purchasedthe deal d under the incentive i, and

${{profit}_{u,d}(i)} = \left\{ \begin{matrix}{- {{cost}_{u,d}(i)}} & {{if}\mspace{14mu} u\mspace{14mu}{reached}\mspace{14mu}{goal}_{i}} \\{{margin}_{d} \cdot {price}_{d}} & {else}\end{matrix} \right.$

Choosing an incentive that maximizes profit directly is notstraightforward. Because of this, the service provider system simulatesthe behavior of the network under various incentives to determineoutcomes for i*. The optimization engine applies graph models to thedataset for a particular network scenario of the service provider systemto simulate outcomes for different parameters and incentives, asoutlined below. For example, in a simple scenario where the profitmargin is 1 (meaning the merchant gets nothing, an unlikely scenario),the profit when a user obtains the free deal is simply 0, as the brokergives the free deal at no cost. Conversely, if the profit margin is 0.5and the user obtains a free deal, then not only is profit margin lost,but the merchant must also be reimbursed their portion of the dealprice. Thus, the system seeks to effectively balance the costs betweenincentivizing the users with the gains from the incentivization.

Network Modeling:

A network resulting from incentivized sharing differs from one ofnon-incentivized shares. Sharing in a network may be altruistic, meaningit has no monetary incentive, or incentivized. Altruistic sharingdistributions follow a power law distribution. Users who share beforepurchasing a deal are sharing because the receiving party might beinterested in the deal and are referred to as non-incentivized shares.If a share occurs after a purchase of a deal, then these post-purchaseshares are incentivized. Within such a network a large number of sendersshare with a handful of recipients and far fewer senders that share witha large number of recipients. However, with the examples describedherein, the sharing network as compared to other networks has a sharperor steeper distribution. The majority of conventional social networkstypically have a power law exponent alpha where 2≤ alpha ≤3; incontrast, the exponent for the networks described herein areconsiderably higher (e.g., 3.5). This indicates users send deals tosubsets of their friends rather than everyone they know.

An example of an incentivized sharing distribution for Me+N is shown inFIGS. 12a and 12b . The power law distribution beginning at theincentivization N has nearly the same exponent as found in thenon-incentivized case (i.e., less than N), both having an exponentaround, for example, 3.5. Thus, the distribution of incentivized usershas been shifted, resulting in approximately the same distribution, theonly difference being that the distribution has added approximately 2shares to every point. Those who share with less than the number neededto receive a reward (outdegree ∈<N) clearly have an altruistic reason toshare because they do not try to reach the incentive of Me+N, users whoshare with N or more recipients (outdegree ≥N) can reach the incentiveeven if this was not their primary reason for sharing. Incentivizingusers has a large increase in the volume of shares. In addition, anincrease volume of shares occurs with incentivization versus noincentivization. As a result, the incentive drives a significant amountof additional share volume.

As the buyer increases the number of recipients (i.e., outdegree), thebuyer becomes less discriminating choosing good recipients; and, onaverage, the recipients are less likely to purchase the shared deal.That is, buyers with larger numbers of deal recipients become lessdiscriminative in choosing which people receive a deal as they increasethe number of people they share the deal with. As a result, a(truncated) χ² distribution indicates the probability of a recipient'spurchase.

While the goal of sharing incentives is to convince recipients to make apurchase, a subset of users needs to make an initial deal purchasebefore the deal can be shared. Therefore, two processes are used tocategorize various users: user arrival and user awakening. User arrivalsto the network are users who have never purchased from networkpreviously, but for a particular deal, on a particular day, they haveenough interest in the offered deal to sign up for the service andpurchase the deal. In contrast, other purchases (without being therecipient of a share) are referred to as “awakenings.” New user arrivalsare very important to the service provider system, as they provide alarge base of users who can be contacted directly regarding productofferings by the service provider system. A broker that can offer alarge visibility for the deals is also more attractive to merchants whowant their deal to be featured on the service provider system.

The duration between purchases of a user is defined as sleeping. Havinga short period of time between user purchases is also important to dealthe sharing websites, as frequent purchases per user keep a user engagedand can drive larger volume of sales. An example of an arrivaldistribution is shown in FIG. 12f , and an awakening distribution isshown in FIG. 12 g.

Optimization Processes

While incentivized sharing through incentivized programs may lead toincreased profits and a greater volume of shares than in thenon-incentivized case, it is difficult to gauge how different incentivescenarios affect the broker's profits. For example, although sharing isgenerally considered positive for a social network (i.e., since sharinggives greater exposure to the website and increases the number ofpurchases), if the majority of senders achieve a free deal while veryfew recipients purchase an offer, revenue can suffer despite the overallincrease in volume of transactions. Therefore, the system and methodsdescribed herein implement a formal graph model to simulate the behaviorof users and determine which scenarios a broker network should implementto maximize or optimize revenue. Accordingly, a generalized graph modelis implemented that rewards users if N of their friends buy their shareddeal (i.e., Me+N). The model is designed to be modular, in order to testand compare different scenarios with respect to incentivized sharing.Thus, the model may use different statistics corresponding to the userbehavior of a company interested in putting monetary incentives inplace.

An optimization process implemented by the optimization engine of theservice provider system is shown in FIGS. 13 and 14. FIG. 13 shows oneexample 1300 of an optimization process. FIG. 14 shows one example 1400of a modeling process that is used in the optimization process of FIG.13. The processes may be adapted to any model and dataset simply bychanging the input parameters and datasets. As a result, the processesare versatile and able to provide simulations for modeling any number ofdifferent incentivization scenarios.

As shown in FIG. 13, the optimization engine 235 accesses data forsharing via a broker network, for example, from a database, such as 236,237 (1301). The optimization engine 235 selects a particular incentivesharing scenario or model for processing (1305). The optimization engine235 processes incentive model for sharing based on model parameters andnetwork data to determine profitability for model using variousdistributions to model the incentive behavior for any particular dataset(1310). The optimization engine 235 stores the determined profitabilityin a particular slot of matrix for the incentive models corresponding tothe dataset just processed (1315). The optimization engine 235determines whether all datasets have been processed and the matrixcompleted (1320). After the matrix has been completed, the optimizationengine 235 processes the matrix to determine profitable incentive modelsfrom the processed scenarios (1325). The optimization engine 235 may beconfigured to provide selection of a threshold for sharing and anincentive/reward from one profitable model to be implemented by thesystem service provider 110 (1330). The system service provider 110provides the selected incentivized sharing deal to a plurality of usersof social network (1335).

FIG. 14 shows one example 1400 of a modeling process that is used in theoptimization process of FIG. 13. For example, the process shown in FIG.14 may be used to implement the processing of 1310 in the process ofFIG. 13. The process 1400 is used by the optimization engine 235 toprocesses a particular incentive model for sharing based on modelparameters and network data to determine profitability for the model fora designated time period (e.g., a week, a month, a quarter, a year,among others) and dataset.

First, the time period and parameters for the network model aredetermined and loaded from the accessed network data and via aninterface provided to a broker (1401). The time unit is set to zero(1405). The time period (e.g., a day) is incremented one unit (1410).The number of users is determined for the time unit (1415). The numberof purchases for the determined number of users is determined and addedto the profit (1420). The number of shares for users making purchase isdetermined (1425). The number of purchases of persons with whom deal isshared is determined and added to the profit (1430). The number of usersreaching the incentive is determined and the cost of the achieved rewardis subtracted from the profit to determine the total daily profit(1435). The total profit is incremented by the determined total dailyprofit (1440) and it is determined whether the end of period is reached(1445). If not, the process 1410-1440 is repeated for the next time unit(e.g., the next day in the time period). After all time units have beenprocessed, the total profit for the time period is returned for theselected parameters of the broker network (1450).

Examples of the various functions and distributions that may be used byoptimization engine and service provider system to implement theprocessing 1405-1440 are shown in the following table:

Notation Description R(o, d) The set of recipients for a deal d sharedwith them by sender o θ The parameters needed by the sharing graphmodel. Arr(θ) The arrival function - how do new nodes enter our network?Awk(θ) The awaken function - how do existing nodes decide to make apurchase? Incen(θ) The incentive function - is the user incentivized?Share(u_(i), θ) The sharing function - how many nodes does a purchasershare with? RPrch(u_(i), θ) The recipient purchasing function.Cost(r_(p), θ) The cost functionThe following is one example of a routine that may be used to implementthe processes of FIG. 14:

1: # Initialize 2: nodes = [ ], purchase = [ ], incent = [ ] 3: profit =0 4: # Run simulation for some period of time 5: for all day in days do6:  activatednodes = Arr(θ) 7:  for all node in activatednodes do 8:  incent[node] = Incen(θ) 9:  end for 10:  # Awaken the nodes for theday 11:  awakenednodes = Awk(θ) 12:  dailynodes = activatednodes +awakenednodes 13:  profit += θ.purchase_profit * len(dailynodes) 14:  #Determine all sharing for the day 15:  sp, sl = Sharing(dailynodes,incent, day, θ) 16:  profit += sp 17:  profit −= sl 18: end for 19:return profitThe following is one example of a second routine that may be used toimplement the determination of the sharing for the day in the firstroutine:

1: # Initialize 2: profit=0, loss = 0,purchases = [ ] 3: # Determinesharing for each node 4: for all node in dailynodes do 5:  r =Share(incent[node], θ) 6:  # Store purchases from shared 7:  r_(p) =RPrch(r, θ) 8:  profit += θ.purchase_profit * len(r_(p)) 9:  loss +=Cost(r_(p), θ) 10: end for 11: return profit, loss

For simplicity, as well as generalization to other datasets, allparameters for the routines are provided in dataset θ where they areaccessed by any function of the routine that needs them. As explainedabove, the model is a temporal generator of data. The functionsimplementing the model are explicitly modular, meaning the statisticscan be mined or postulated for a given dataset and swapped, allowingcomparison for differing behaviors of any desired scenario. The mainloop of the first routine (e.g., lines 5-19 loopstart through loopend)captures the daily behavior of users in the model. More precisely, theloop codifies 1) the behavior of users within the social network asdeals are presented to them, 2) how they choose to share those deals,and 3) how those shares result in additional purchases.

Within the main loop, the daily behavior of the model has a number offactors: Arrival; Sleeping; Sharing; Recipient Purchasing, and Profits.Arrival indicates how new users join the network (i.e., when they maketheir first purchase). Sleeping and Awakening indicate the periodbetween purchases. Sharing indicates what drives the sharing behavior ofusers. Recipient Purchasing indicates how recipients choose to purchasethe shared deal. Profits indicate how purchases and the incentivesaffect the overall profits.

Arrival and Awakening Functions

The Arrival and Awaken properties, which were discussed above, areexplicitly incorporated in the model (e.g., lines 6 and 11 of the firstroutine). The arrival and awakened users are users that choose to buyfrom the website on a particular day of their own accord. A variety offunctions may be used for the arrival function Arr. One example of thearrival function is a function with a constant arrival sequence:Arr(θ)=θ·Xwhere θ·X is a non-negative integer representing a constant daily numberof users who join the website service and make their first purchase. Ofcourse, alternative arrival functions may be used. Another function maybe used to model when the arrival is a random variable from adistribution. For example, arrival functions in social networks can varyfrom exponential to sub-linear growth. In addition, Gaussian or thePoisson also may be used to model arrival rates, for example:Arr(θ)=round(N(θ·μ,θ·σ))Arr(θ)=poisson(θ·λ)Another alternative may be used to provide for seasonal effects on thearrival function, such as the distribution of particular days of theweek or months of the year. For example, many retail goods experiencegreater volume of sales surrounding December, as compared to othermonths. Therefore, such an arrival function can be modeled as:Arr(θ)=round(N(θ·μ_(θ·d),θ·σ_(θ·d)))where θ·d represents the day of the week, month of year, or any otherrelevant time-dependent distributional change.

One example of the awaken function is Awk:Awk(θ)=θ·d+x ²(θ·k)The awaken function need not take into account the delay betweenpurchases as its primary decision. In another example, an alternativeawaken function may determine whether a user is likely to awaken andpurchase a deal based on the likelihood of a user purchasing the offereddeal:Awk(u,d,θ)=P(u|d,θ)Such a model is useful when users purchasing behaviors can be fit to apreference model, that is, users choose deals to purchase based on theirown intrinsic interests. Preference models have been shown to havepredictive power on the purchasing patterns of deal sharing sites.

In order to compare and contrast between sharing distributions that canbe created by various scenarios, the model takes into account how theincentive affects the sharing behavior of users. Modifications of theincentive threshold, N, in Me+N results in a distribution shift for theincentivized shares while leaving the exponent intact (the shift can becharacterized as a constant), as shown in FIGS. 12a and 12b . There alsomay be deal parameters that can change the volume of additional shares,such as deal price and deal popularity. As a result, these also may beaccounted for in the model. In addition, as N increases, the additionalamount of incentivized shares at any given outdegree decreases, and thealtruistic share proportion increases.

It is also important to determine which users share because of theincentive and which ones share in an altruistic manner, so that theincentivized sharing distributions can be generated. An example ofaltruistic sharing probabilities is shown in FIG. 12c . Two basicimplementations use these probabilities that create the correct sharingdistribution; however, others can be used. In one example, a methodassumes a fixed motivation for each user; that is, every user who joinsthe network is either altruistic by nature or only responds toincentives. The variable, incent[node] is then stored and used when theuser initiates a deal sharing. To determine the number of sharerecipients, recip, a number is drawn from a power law distribution.Should the user be incentivized, then N−1 is added to the number drawn,otherwise the same value is used. Thus, users who respond to incentivesare drawn from a power law with a minimum value N, while altruisticvalues are drawn from the power law with a minimum value of 1.Incen(θ)=Ber(θ·p)Share(θ)=PL(θ·cx)+I[θ·b]·(θ·N−1)As an alternative, a user can change whether they are incentivized perdeal, ignoring their past sharing behavior. For example:Share(θ)=PL(θ·cx)+Ber[θ·p]·(θ·N−1)Other possibilities can assign a probability of repeating their previousvalue, or switching. Letting θ·q be the probability the user keeps thesame incentivization behavior, provides:Share(θ)=PL(θ·cx)+Ber(θ·q)^(θ·b)·(θ·N−1)Such behavior can be useful for modeling accurate incentive behaviorsfor the users as they change over time.

Another important function in the model is the number of successfulpurchase recommendations given the number of recipients. That is, if thesender shares a deal with four other people, what is the probability noone will purchase the deal, one person will purchase the deal, twopeople will purchase the deal, and so on. FIG. 12d shows an example ofthe purchase probability distributions for sender-deal shares with 4, 5,6 and 7 recipients. These distributions are best described by a χ²distribution, however, the distribution is truncated for values greaterthan the number of shares (since there cannot be more purchasers of thedeal than were shared with). Accordingly:RPrch(θ)−truncated(x ²(θ·k),θ·s)where θ·s is the outdegree of the sender-deal pair, and θ·k is theparameter to the truncated χ² distribution.

As seen in FIG. 12a , the probability of a recipient purchasing a dealnoticeably decreases as the outdegree of the sender-deal pair increases.Furthermore, FIG. 12b shows that despite the overall purchaseprobability decreasing, the probability of the sender acquiring a freedeal increased as the outdegree increased. However, generally, thepurchase probabilities for the recipients are invariant with respect tothe incentive. A plot of the lift probabilities of free deals for avariety of N values can be characterized as:

${{Lift}\left( {s,\theta} \right)} = \frac{P\left( {\left. {free\_ deal} \middle| s \right.,\theta} \right)}{P\left( {free\_ deal} \middle| \theta \right)}$That is, what is the ratio of the free deal probability given a certainnumber of shared against the total free deal probability. This is shownin FIG. 12e . Thus, while it is likely that certain users plan to buy adeal without using the broker interface, these users are small incomparison to overall body of users.

In addition, the free deal probabilities have a general upward trend foreach of the N values. While not exhaustive, having a consistent behaviorfor each of the varying N values implies that the free dealprobabilities are independent of N.

Sharing incentives naturally result in a variety of costs to the broker.The simplest example is where the deal is given out freely to the user,and the reward cost is equal to the deal price. The sharing costfunction is Cost(rp,θ), where r_(p) is the number of recipients whopurchased a deal recommended to them by a sender, and θ is the set ofparameters, such as the incentive threshold or the number of purchaserswho must buy a deal in order for the sender to receive the reward. Moreformally, the cost function for the reward function described above canbe defined as:Cost(r _(p),θ)=II[∥r _(p) ∥≥θ·N]·$θ·rewardwhere II is the indicator and θ·N is the incentive threshold. As analternative cost function, a fixed cost function may be used. In thismodel, the reward is not related to the price of the deal or themerchant percentage. Such an incentive has a reward of a constantamount, for example:Cost(r _(p),θ)=II[∥r _(p) ∥≥θ·N]·$10Fore example, such a method is used by the refer-a-friend program byBloomspot, Fab, and LivingSocial for new user acquisition.

Accordingly, the above described functions for arrivals, awakened users,incentives, sharing, recipient purchasing, and cost are used to model asharing scenario for a set of parameters. These scenarios are thencompared to determine optimal candidates for actual implementation andrefinement.

Simulated Examples

The flowing section provides some examples for illustration of themodels given above. In the following example, comparisons are made thatvary 1) the incentive threshold value, N, of the Me+N, and 2) thesharing distributions under various starting conditions for the models.The models are compared by examining which model consistentlyoutperforms the others in terms of profits.

For example, assume a constant deal price of $50, an arrival function of10000 new users per day, with a X²(50) awakening distribution, theprofit margin set to 50%, and the reward fixed to be equal to the dealprice. In these examples, 300 deals/day are simulated 50 times for eachmodel. Also assume for simplicity of illustration that the distributionsare price-invariant (although modeling can be set to adjust for priceeffects of both share volume and structure). Two models (Diminished andFlat) of projected sharing behavior for the other +N cases as outlinedabove and shown in FIGS. 12a-e are created. As shown in FIGS. 15a-c ,“Diminished” is denoted as when the incentivized sharing converges tothe non-incentivized sharing, and “Flat” denotes when it does not.According to the following examples, one or more of these assumptionsare varied to provide comparison between differing scenarios.

In a first example, a cost model is used to represent a scenario whenthe broker receives a portion of the deal price with the remainderprovided to the merchant offering the service. Under such a model, theservice provider system and broker must reimburse the merchant for theirportion of a deal cost when a customer achieves a free deal. Thus, whena profit margin is high, or the broker receives nearly the entire dealcost, the model performs similarly as in the case where the brokerreceives the entire profit. Conversely, when the profit margin is low,the merchant receives the majority of the deal cost, resulting in a freedeal having a large impact on the broker's revenues.

FIGS. 15a, 15b, and 15c illustrate the results when the percentage ofthe profits the broker receives is varied. When the profit margin ishigh as shown in FIG. 15a , performance is generally better as thevolume of shares increases. The exception is in the Me+2 case, which isdiscussed below. Aside from that case, the ‘Flat’ sharing distributiongenerally acts as an upper bound to performance, while the ‘Diminished’functions as a lower bound. As the profit margin decreases, a higherpenalty is incurred for giving away the deal. FIG. 15c shows that as theprofit margin goes to 0, the broker loses more money from free dealsthan it gains from the additional purchases. In this situation, itbecomes more profitable to omit any incentivization. In one scenario,using a 50% profit margin shown in FIG. 15b , the Me+3 outperforms theMe+4 and Me+5. FIG. 15d shows the performance when the reward given backto the individual is limited to 20% of the deal price.

One point of interest illustrated by FIGS. 15a-c is that the behavior ofthe Diminished and Flat models are very similar across the profitmargins in terms of performance despite the change in the volume ofshares. FIGS. 16a and 16b further illustrate this showing what happenswhen the profit margin is fixed at 50%, and the percentage ofincentivized users is varied from 30% to 100%. In the Me+3 case, thereis a steady, linear increase in the profits received based on thepercentage of users which have been incentivized. However, in the Me+2case, there is little to no increase in profits as the volume ofincentivized users is increased, despite the fact that share volumeincreases considerably. As such, the Me+2 case is in an equilibriumstate. The penalty from the ease of attaining the reward outweighs theadditional purchases from the additional shares received.

FIG. 16c examines the profits as both the reward (as a percentage ofprice) and number of incentivized users linearly increase, starting witha reward of 20% of the price coupled with 10% of users beingincentivized. As both percentages are increased, Me+3 increases as well.This shows that the overall gain from the additional shares outweighsthe negative impact of the additional cost of the reward. However, withMe+2 the opposite is true. In this case, as the reward increases, adecrease in profitability is shown as the additional incentive costoutweighs any additional purchases. FIG. 16d shows effects with theincrease in shares.

In the previous examples, the incentive cost typically outweighed thebenefit of the Me+2 sharing incentivization. However, there is ascenario where Me+2 can perform equally well as the Me+3; namely, whenthe incentive cost is low, the additional volume of shares can overpowerthe loss as illustrated in FIG. 15d . FIG. 15d shows the reward offeredto the user to be 20% of the deal price. In this scenario, Me+2 performscomparably, or better, than Me+3. As such, when the reward offered tothe users is low in comparison to the deal profit, a more aggressiveincentive scheme should be used. Additionally, this model assumes fixedsharing distributions; should the sharing behavior change dramaticallyas the incentive amount changes these results may change.

Overall, in the examples provided above, the model provides simulationof the tradeoffs between the large volume of shares, which result from ahighly attractive incentive, versus the high cost of that incentive. Asthe merchant is able to receive a larger percentage of the deal price,the cost of having a large incentive becomes prohibitive. This isimportant, as users are ignorant to the background costs of running adeal sharing broker network. Furthermore, the fixed cost of a reward canbecome prohibitive to sharing as well. Thus, as a deal sharing networkcontemplates offering such an incentivized sharing model, the networkshould consider the profit margin received for that deal, the volumeincrease or decrease from the incentivization scheme, as well as thelikelihood of users achieving the incentive goal.

Optimization Scenario

The following is one simple example for illustrative purposes only. Forexample, assume a service provider has some organic/altruistic sharingof products/services and purchases (i.e., product adoption) occurring asthe result of the sharing within its network of users. In this sense,the sharing is organic as there is “no incentive” scenario implemented.The service provider would like to incentivize sharing to increase thesharing and adoption of products within its network of user; however,the service provider does not want to lose money in this process.

The service provider adopts the Me+N sharing model as described herein,which takes as input the parameters of organic sharing of the existingnetwork to predict the effect of an incentive on profits using themodel. Using the processes described above with regard to FIGS. 13 and14, for example, the optimization engine determines the model outputs,for different projected additional shares, what the optimal incentive isand under what incentives the company is not losing money (paying morerewards than it receives in referred revenue).

For example, the process in FIG. 13 generates a matrix (for 50% profitmargin):

% additional shares due to incentive 0% 50% 100% Optimal No incentiveMe + 3 Me + 2 Better than ‘no N/A Me + 3, Me + 4, etc. Me + 2, Me + 3,etc incentive’ Positive profit Me + 2, Me + 2, Me + 3, etc. Me + 1, Me +2, etc. Me + 3, etc.

In this example, “Optimal” is defined as the incentive that maximizesprofits including no incentive is an option. “Better than organic” isdefined as the incentive scenario(s) that have higher profit thanorganic (i.e., revenue from additional referred purchases due to theincentive is higher than the cost paid for rewards). “Positive profit”is defined as the incentive(s) in which revenue from referred purchases(based on both organic shares and additional shares due to incentive) ishigher than the cost paid for rewards.

The matrix is analyzed to consider those incentives scenarios that arealways in positive profit and better than no incentive for someassumptions on the % additional shares. In the example above, Me+3 andN>3 are better than ‘no incentive’ in both 50% and 100% additional. Onescenarios these scenarios is then selected. In addition, A/B testing maythen be used to collect information on % additional sharing for each Ndetermine where in the matrix hey actually fit. Of course, one willappreciate that this matrix is illustrative only and that more granulardemarcations may be determined. For example, the % additional sharingfor each N may be broken into smaller increments (e.g., 10% increments).

Further Examples

With regard to the implementations given above, it will be appreciatedthat the examples are illustrative, and that any number of, types of, orconfigurations of different components and software may be incorporatedinto or omitted from any particular device. For example, the user device101 or server 205 may include a number of components including one ormore of the following: one or more processing devices, one or morestorage devices, and one or more communications interfaces. The devicesalso may include additional elements, such as one or more input devices(e.g., a display, a keyboard, a key pad, a mouse, a pointer device, atrackball, a joystick, a touch screen, microphone, etc.), one or moreoutput devices (e.g., speakers), a display, one or more interfaces,communications buses, controllers, removable storage devices, and atleast one power source. Additional elements not shown may includecomponents of a digital camera, an optical reader (e.g., a bar codescanner or an infrared scanner), an RFID reader, andantennas/transmitters and/or transceiver. A user device also may includeone or more associated peripheral devices (not shown), such as, forexample, a display, a memory, a printer, an input device, an outputdevice, and speakers. As is appreciated by those skilled in the art, anyof these components (other than at least one processing device) may beincluded or omitted to create different configurations or types of userdevices, for example, to perform specific or specialized needs or tasks,generalized needs or multiuse tasks, or for various performancecriteria, such as, mobility, speed, cost, efficiency, power consumption,ease of use, among others.

A processing device may be implemented using one or more general-purposeor special purpose computers, such as, for example, a processor, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a field programmable array, a programmable logic unit, amicroprocessor or any other device capable of responding to andexecuting instructions in a defined manner. The processing device mayrun an operating system (OS) and one or more software applications thatrun on the OS. The processing device also may access, store, manipulate,process, and create data in response to execution of the applications.

For purpose of simplicity, the description of a processing device isused as singular; however, one skilled in the art will appreciated thata processing device may include multiple processing elements or devicesand multiple types of processing elements or devices. For example, aprocessing device may include multiple processors or a processor and acontroller. In addition, different processing configurations arepossible, such as serial processers, parallel processors, a quadprocessor, a main processor and a display processor or cachingprocessor, among others. As used herein, a processing device configuredto implement a function A includes a processor programmed to runspecific software. In addition, a processing device configured toimplement a function A, a function B, and a function C may includeconfigurations, such as, for example, a processor configured toimplement both functions A, B, and C, a first processor configured toimplement function A, and a second processor configured to implementfunctions B and C, a first processor to implement functions A, a secondprocessor configured to implement function B, and a third processorconfigured to implement function C, a first processor configured toimplement functions A and B, and a second processor configured toimplement function C, a first processor configured to implementfunctions A, B, C, and a second processor configured to implementfunctions A, B, and C, and so on.

The software applications may include a computer program, a piece ofcode, an instruction, or some combination thereof, for independently orcollectively instructing or configuring the processing device to operateas desired. Examples of software applications include: an operatingsystem, drivers to control and/or operate various components of the userdevice (e.g., display, communications interface, input/output devices,etc.).

In particular, an optimization engine may operate an applicationimplement the processes in FIGS. 13 and 14 and run routines 1 and 2. Inaddition, applications may implement an interface, such as a browser, amini browser, a mobile device browser, a widget, or other programs thatinteract with the service provider systems to provide or present contentand a user interface or conduit for presenting deals from the broker.The interfaces may include, among other features, browser based tools,plug-ins, and extension applications, such as Java, Acrobat Reader,QuickTime, or Windows Media Player, and a Flash Player (e.g., Adobe orMacromedia Flash).

The applications may be resident in the processing device, loaded from astorage device, or accessed from a remote location or a storage device,as described in greater detail below. Once the applications, such as abrowser, are loaded in or executed by the processing device, theprocessing device becomes a specific machine or apparatus configured toperform a function, such as to provide a user interface to render,present, provide, and interact with content from a content providersystem. That is to say a user device with a processing device programmedin a certain way is physically different machine than that of a userdevice without that program as its memory elements are differentlyarranged and/or configured.

The software, applications, content, and data may be embodiedpermanently or temporarily in any type of machine, component, physicalor virtual equipment, computer storage medium or device, or in apropagated signal wave capable of providing instructions or data to orbeing interpreted by the processing device. In particular, the software,applications, content, or data may be stored by one or more computerstorage devices including volatile and non-volatile memories that storedigital data (e.g., a read only memory (ROM), a random access memory(RAM), a flash memory, a floppy disk, a hard disk, a compact disk, atape, a DROM, a flip-flop, a register, a buffer, an SRAM, a DRAM, anSSD, a PROM, a EPROM, a OPTROM, a EEPROM, a NOVRAM, or a RAMBUS, suchthat if the storage device is read or accessed by the processing device,the specified steps, processes, and/or instructions are performed and/ordata is accessed, processed, and/or stored. The computer storage devicemay include an I/O interface, such that data and applications may beloaded and stored in or accessed or read from the computer storagedevice allowing the applications, programming, and data to be used,updated, deleted, changed, augmented, or otherwise manipulated. Thecomputer storage device may be removable, such as, for example, a diskdrive, a card, a stick, or a disk that is inserted in or removed fromthe user device.

A communications interface may be used to exchange data and content withthe content provider system or user devices using various communicationpaths 115. The communications interface may be implemented as part ofthe processing device or separately to allow the processing device tocommunicate or send and receive data using the communication paths 115.The communications interface may include two or more types ofinterfaces, including interfaces for different types of hardware and/orsoftware to interact with different types of communications media andprotocols and to translate information/data into a format that may beused by the processing device. Similarly, the interface may translateinformation/data received from the processing device to a format thatmay be transmitted to the service provider system via a communicationpath.

The communication paths 115 may be configured to send and receivesignals (e.g., electrical, acoustic, electromagnetic, or optical) thatconvey or carry data representing various types of analog and/or digitaldata including programming, software, media, and content, among others,for presentation to a user. For example, the communication paths may beimplemented using various communications media and one or more networkscomprising one or more network devices (e.g., network interface cards,fiber media converter, servers, routers, switches, hubs, bridges,repeaters, blades, processors, and storage devices). The one or morenetworks may include a local area network (LAN), a wide area network(WAN), an Ethernet, a global area network (GAN), a cloud network, aplain old telephone service (POTS) network, a digital subscriber line(DSL) network, an integrated services digital network (ISDN), asynchronous optical network (SONNET)/SDH, Passive and Active OpticalNetworks (PON or AON), a packet switched network, V.92 telephone networkmodems, IRDA, USB, Firewire, EIA RS-232, EIA-422, EIA-423, RS-449,RS-485, ITU, T1 and other T-carrier links, and E1 and other E-carrierlinks, varieties of 802.11, GSM Um radio interface, Bluetooth, IEEE802.11x Wi-Fi, TransferJet, Etherloop, ARINC 818 Avionics Digital VideoBus, G.hn/G.9960, or a combination of two or more of these networks toname a few.

In addition, the communication paths may include one or more wirelesslinks (e.g., microwave, radio, and satellite) that transmit and receiveelectromagnetic signals, such as, for example, radio frequency,infrared, and microwave signals, to convey information/data signal usingany one of a number of communications protocols, for example,communications links may include IMT-2000, such as 2G (GSM, GPRS, EDGE,EDGE Evolution, CSD, HSCSD), 2.5G, 2.75G, 3G (W-CDMA, HSPDA, HSUPA,UMTS-TDD, FOMA), 4G, and IEEE 802.11 standards, such as Wi-Fi or WLAN.In one example, a communication path may include the Internet or WorldWide Web or components found therein.

Data and content may be exchanged between the service provider system110 and the user device 101 through the communication interface andcommunication paths using any one of a number of communicationsprotocols. In particular, data may be exchanged using a protocol usedfor communicating data across a packet-switched internetwork using, forexample, the Internet Protocol Suite, also referred to as TCP/IP. Thedata and content may be delivered using datagrams (or packets) from thesource host to the destination host solely based on their addresses. Forthis purpose the Internet Protocol defines addressing methods andstructures for datagram encapsulation. Of course other protocols alsomay be used. Examples of an Internet protocol include Internet ProtocolVersion 4 (IPv4) and Internet Protocol Version 6 (IPv6).

A number of exemplary implementations have been described. Nevertheless,it will be understood that various modifications may be made. Forexample, suitable results may be achieved if the steps of describedtechniques are performed in a different order and/or if components in adescribed components, architecture, or devices are combined in adifferent manner and/or replaced or supplemented by other components.Accordingly, other implementations are within the scope of the followingclaims.

What is claimed is:
 1. A computer implemented method of simulating andoptimizing an online incentive network without a need to concentrate onidentifying a relatively small set of individuals with high networkvalues and incentivizing the relatively small set of individuals toadopt a product, in the hope that the small set of individuals wouldinfluence many others to also adopt this product, the method ofsimulating and optimizing the online incentive network performed via anincentivized sharing model that (i) takes as input, parameters oforganic sharing of an existing network to predict an effect of anincentive and (ii) provides, after purchasing a product, a reward if theuser provides, via a dedicated link, an offer for the product to otherusers resulting in at least a threshold number (N) or more otherspurchasing the product, the method comprising: accessing onlineincentive network data including data regarding sharing between users ofthe online incentive network from at least one database; inputtingparameters and the accessed online incentive network data to one or moreprocessing devices hosted by a deal broker; applying, by one or more ofthe processing devices, the parameters and the accessed online incentivenetwork data to each of a plurality of graph models for incentivizeddeal sharing, wherein each of the graph models are configured toaccurately determine likely outcomes of various incentive scenarios, thevarious incentive scenarios associated with a plurality of values forthe threshold number of the other users, the threshold number of theother users being a number of other users that must purchase the dealfor the user to receive the incentive, wherein each of the plurality ofgraph models are modular and configured to use data corresponding touser behavior of a particular company interested in putting monetaryincentives in place, enabling testing and comparing of the variousincentive scenarios, by: simulating, by one or more processing devices,deal sharing for various scenarios of parameters, including selecting avalue for the threshold number of other users that must purchase thedeal for the user to receive the incentive, and the accessed onlineincentive network data in the online incentive network; determining, byone or more of the processing devices, a deal broker's profitability ofeach simulated scenario; storing the determined deal broker'sprofitability of each simulated scenario in a decision matrix; receivingfeedback through additional accumulated data to modify each simulatedscenario in the decision matrix based on actual performance associatedwith the particular deal; identifying, from among a subset of eachsimulated scenario meeting a predefined threshold, at least oneprofitable model to be implemented; and selecting, by one or more of theprocessing devices, a simulated scenario based on the determinedprofitability of each simulated scenario identified as meeting thepredefined threshold, the selected simulated scenario being one thatwould increase the deal broker's profitability to an acceptable level,wherein selecting the simulated scenario comprises selecting one or moreparameters including the selected value for the threshold number ofusers that must purchase the deal for the user to receive the incentiveby determining the selected value at which an additional volume ofshares outweigh an associated incentive cost; and providing a graphmodel associated with the selected one or more parameters of theincentive sharing scenario, including the selected value for thethreshold number of other users, to the users of the online incentivenetwork includes by way of the deal broker to an online or mobileinterface associated with each user of the online incentive network. 2.The method of claim 1 wherein the parameters include a price of thedeal; a proportion of the deal kept by the merchant or the onlineincentive network, and the threshold number of other users who mustpurchase the same deal from the merchant that a user purchases beforethe user is provided with a reward.
 3. The method of claim 1 whereinsimulating each scenario comprises determining a number of usersentering the online incentive network.
 4. The method of claim 1 whereinsimulating each scenario comprises determining how many existing userspurchase a deal.
 5. The method of claim 1 wherein simulating eachscenario comprises determining how many users are incentivized.
 6. Themethod of claim 5 wherein simulating each scenario comprises determininghow many users the incentivized users share the deal with.
 7. The methodof claim 6 wherein simulating each scenario comprises determining howmany recipients of the shared deal purchase the shared deal.
 8. Themethod of claim 7 wherein simulating each scenario comprises determininghow many users who shared the deal receive the incentive.
 9. An onlineservice provider system configured to simulate and optimize an onlineincentive network without a need to concentrate on identifying arelatively small set of individuals with high network values andincentivizing the relatively small set of individuals to adopt aproduct, in the hope that the small set of individuals would influencemany others to also adopt this product, the method of simulating andoptimizing the online incentive network performed via an incentivizedsharing model that (i) takes as input, parameters of organic sharing ofan existing network to predict an effect of an incentive and (ii)provides, after purchasing a product, a reward if the user provides, viaa dedicated link, an offer for the product to other users resulting inat least a threshold number (N) or more others purchasing the product,the system comprising: one or more storage devices hosted by a dealbroker and configured to store online incentive network data includingdata regarding sharing between users of the online incentive network;one or more storage devices hosted by the deal broker and including oneor more applications configured to optimize a graph model forincentivized deal sharing for one or more products or services providedby a merchant, wherein the graph model comprises providing a user, afterthat user purchases a particular deal from a merchant, a reward only ifthat user shares the particular deal with a threshold number of otherusers who also purchase the deal from the merchant; and one or moreprocessing devices hosted by the deal broker and configured to: receiveone or more input parameters and the stored online incentive networkdata; apply the parameters and the stored online incentive network datato each of a plurality of graph models for incentivized deal sharingeach of the graph models are configured to accurately determine likelyoutcomes of various incentive scenarios, the various incentive scenarioassociated with a plurality of values for the threshold number of theother users, the threshold number of the other users being a number ofother users that must purchase the deal for the user to receive theincentive, wherein each of the plurality of graph models are modular andconfigured to use data corresponding to user behavior of a particularcompany interested in putting monetary incentives in place, enablingtesting and comparing of the various incentive scenarios, by: simulatingeach of a plurality of scenarios based on the parameters, includingselecting a value for the threshold number of other users that mustpurchase the deal for the user to receive the incentive, and the storedonline incentive network data in the online incentive network;determining a deal broker's profitability of each simulated scenario;storing the determined deal broker's profitability of each simulatedscenario in a decision matrix; receiving feedback through additionalaccumulated data to modify each simulated scenario in the decisionmatrix based on actual performance associated with the particular deal;identifying, from among a subset of each simulated scenario meeting apredefined threshold, at least one profitable model to be implemented;and selecting a simulated scenario based on the determined profitabilityof each simulated scenario identified as meeting the predefinedthreshold, the selected simulated scenario being one that would increasethe deal broker's profitability to an acceptable level, whereinselecting the simulated scenario includes selecting one or moreparameters of the incentive sharing scenario including the selectedvalue for the threshold number of users that must purchase the deal forthe user to receive the incentive by determining the selected value atwhich an additional volume of shares outweigh an associated incentivecost; and provide, by way of the deal broker, a graph model associatedwith the selected one or more parameters of the incentive sharingscenario, including the selected value for the threshold number of otherusers, to the users of the online incentive network including to anonline or mobile interface associated with each user of the onlineincentive network.
 10. The system of claim 9 wherein the parametersinclude: a price of the deal; a proportion of the deal kept by themerchant or the online incentive network; and the threshold number ofother users who must purchase the same deal from the merchant that auser purchases before that user is provided with the reward.
 11. Thesystem of claim 9 wherein the one or more processing devices simulateeach scenario at least in part by determining the number of userentering the network.
 12. The system of claim 9 wherein the one or moreprocessing devices simulate each scenario at least in part bydetermining how many existing users purchase a deal.
 13. The system ofclaim 9 wherein the one or more processing devices simulate eachscenario at least in part by: determining how many users areincentivized; determining how many users the incentivized users sharethe deal with; determining how many recipients of the shared dealpurchase the shared deal; and determining how many users who shared thedeal receive the incentive.
 14. The method of claim 1, whereindetermining the deal broker's profitability of a simulated scenariocomprises one or more of: increasing the profitability as a number ofpurchases of the deal is increased; increasing the profitability as anumber of shares of the deal is increased; increasing the profitabilityas a number of purchases of persons with whom the deal is shared isincreased; and decreasing the profitability for each reward provided toa user who has convinced a threshold number of other users to purchasethe same deal.
 15. The method of claim 1, further comprising: storingthe determined deal broker's profitability of each simulated scenario ina decision matrix; and comparing the profitabilities of the decisionmatrix with each other; wherein selecting the graph model is based onthe comparison.
 16. The system of claim 9, wherein the one or moreprocessing devices are configured to determine the deal broker'sprofitability of a simulated scenario by performing one or more of:increasing the profitability as a number of purchases of the deal isincreased; increasing the profitability as a number of shares of thedeal is increased; increasing the profitability as a number of purchasesof persons with whom the deal is shared is increased; and decreasing theprofitability for each reward provided to a user who has convinced athreshold number of other users to purchase the same deal.
 17. Thesystem of claim 9, wherein the one or more processing devices areconfigured to: store the determined deal broker's profitability of eachsimulated scenario in a decision matrix; and compare the profitabilitiesof the decision matrix with each other; wherein the one or moreprocessing devices are configured to select the graph model based on thecomparison.